SymPy: symbolic computing in Python

Aaron Meurer , Christopher P. Smith , Mateusz Paprocki , Ondřej Čertík , Sergey B. Kirpichev , Matthew Rocklin , Amit Kumar , Sergiu Ivanov (1) , Jason K. Moore , Sartaj Singh , Thilina Rathnayake , Sean Vig , Brian E. Granger , Richard P. Muller , Francesco Bonazzi , Harsh Gupta , Shivam Vats , Fredrik Johansson (2) , Fabian Pedregosa (2) , Matthew J. Curry , Andy R. Terrel , Štěpán Roučka , Ashutosh Saboo , Isuru Fernando , Sumith Kulal , Robert Cimrman , Anthony Scopatz

1 UPE - Université Paris-Est
2 Inria - Institut National de Recherche en Informatique et en Automatique

SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.

Domaines: Informatique [cs]; Physique mathématique [math-ph]

Aaron Meurer, Christopher P. Smith, Mateusz Paprocki, Ondřej Čertík, Sergey B. Kirpichev, et al.. SymPy: symbolic computing in Python. PeerJ Comput.Sci., 2017, 3, pp.e103. ⟨10.7717/peerj-cs.103⟩. ⟨hal-01645958⟩